the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Feb 2025
Impact of urban canopy parameters on urbanization induced modifications of climate
Abstract. Urban areas are characterized by modifications of the local climate leading to a so called urban meteorology island (UMI). UMI is the results of different physical properties of surfaces in cities compared to their rural surroundings. In this study we performed a set of multi-year simulations with the Weather Research and Forecast (WRF) model and two urban schemes to investigate the sensitivity of the urban climate modifications (or UMI) on changes in characteristics of the urban environment, described in models by the so-called urban canopy parameters (UCP). Our results reveal a high sensitivity of urban-induced changes in all mentioned meteorological variables to the alterations of UCP. Temperature in urban areas is mainly influenced by changes in urban fraction, roof albedo, green roofs with irrigation and also by anthropogenic heat in winter, with a magnitude around 0.5 °C. On the contrary, urban wind speed is impacted rather by parameters that describe the urban morphology. Our study also shows substantial differences between both urban models used, mainly in urban-induced temperature in winter. The results of the study can also be used as a primary evaluation of different mitigation strategies represented by changes in UCP values. The decrease of urban fraction and the increase of roof albedo seem to be the most suitable possibilities to reduce the intensity of the urban heat island in summer, vegetation-covered roofs have a noticeable impact only if they are also irrigated.
- Preprint
(4846 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (extended)
-
RC1: 'Comment on egusphere-2025-388', Anonymous Referee #1, 03 Mar 2025
reply
Review of the manuscript egusphere-2025-388
Impact of urban canopy parameters on urbanization induced modifications of climate
By Jan Karlicky, Jachym Bareš, and Peter Huszar
Summary: in this study the WRF mesoscale model is employed over a domain in mid to western Europe in order to study the model sensitivity of the modelled urban meteorology island to urban morphological parameter values.
Recommendation: Major revision
Major remarks:
- In the methodology and discussion sections I miss a reasoning how the range of which the UCPs have been varied? Are all parameter values reasonable?
- In the modelling strategy misses how the model simulations were set up in the time domain. I.e. is the experiment based on 1 long free running simulation? Or does it consist of a series of e.g. 48 or 72 h forecasts that have been glued together? Would a different strategy result in a different answer to the research question?
- The paper does not mention spinup of temperature of buildings (brick). In hot summer days, the spin up can take 3 days or more if you make a cold start. Could the authors analyse this in more detail, e.g. through time series analysis of the storage heat flux. If the daily max storage flux is more or less the same for a number of consecutive days, then the spin up is well established.
- On temperature evaluation: at what height was the observation taken, and is that the same height as representative in the model. In the model, did you evaluate the T2M or the air temperature within the canyon? In general the paper could discuss in more detail the discrepancy between local observations, gridded observations (E-OBS) and model results. Their “footprint” is not the same, which cannot be avoided, but should be discussed.
- Ln 115: The resulting UHI intensity… The UHI is not well defined yet in this study. Do you evaluate the statistics on all hourly UHI values? Or do you take the maximum daily UHI (lets say 2-3 h after sunset) and evaluate the statistics over multiple years? Also the study generates UHI statistics for multiple cities across the European domain, so then which grid cells are used to categorize “urban” and “rural”. I.e. how many km near a city is considered and the rural area belonging to a certain city/urban area.
- Concerning the visualizations of the model sensitivities: The model results are now presented as differences in meteorological values between simulations. Why not plot UHI as function of albedo, and other properties, so we learn about a slope between the UHI and the varied parameter, like in Steeneveld et al. (2019). I think it may attracts a wider audience from the field of the city planning and design.
- Apart from the caveats above I find the research quite solid, but I miss a bit where is the innovation in the manuscript. The study does not introduce new and novel observational datasets, neither contributes to new model code. Can this be put forward in more detail?
Minor remarks
Ln 2: UMI is the results -> UMI is the result
Ln 3: Forecast -> Forecasting
Ln 38: builtup -> built up
Ln 42: I think this paper (particularly Fig 5) is a reasonable first attempt (despite in a 1D WRF context): https://doi.org/10.1016/j.resconrec.2016.12.002
Ln 53: horizontal resolution -> I have a little preference here to replace resolution with “grid spacing”. The resolution is then about 5*the grid spacing
Ln 54: 40 model layers : is this not little small these days? Please also document what is the height of the first model level, and the typical number of layers in the daytime PBL.
Ln 61-66: The list of parameterizations is well documented for reproducibility reasons, but at the same time they have not been justified or defended. Why is exactly this combi of settings suitable for answering the research question?
Figure 1: Please add scale bar and North Arrow
Ln 81: “The sensitivity of model simulations on specific urban canopy parameters” -> “The sensitivity of model results to specific urban canopy parameter values”
Table 5: the header needs to say we look at statistics for air temperature. In addition, is it 2-m air temperature? And also: if one verifies against E-OBS, I would expect to only use rural grid cells, not urban grid cells, since E-OBS does not represent cities well.
Section 3.1: I agree these statistics should be presented, but it would be good too to say here something whether the biases are in line with similar WRF studies for European cities. To put the results in a wider context.
Ln 117: but in winter there is an overestimation of simulation with BEP+BEM model with a magnitude above 1.5 ◦C. Do you understand why? Is that due to a poor representation of the anthropogenic heat flux, or a difference in target temperature between model and the real world?
Table 6: I do not understand what the column “stations” represents here. Also I suggest to merge Tables 5 and 6, since the manuscript has many relatively small tables scattered over the manuscript. It is more comprehensive to bring then together.
Figure 2: please label the panels a,b,c, and d. In the caption add that we look at “2-m air temperature” and define winter and summer (DJF and JJA respectively). It is serving the reader that (s)he does not need to go back to the text how winter and summer have been defined.
Ln 123: Add “The” before Planetary Boundary Layer Height
Ln 151: “Significant differences … ”. Has this been tested using a statistical test? Rain is a challenging variable to get sufficient events to make differences significantly visible.
Figures 4-7: It would be helpful for the reader to include in the caption whether this effect is measured over all cities (so both spatially and temporal means) or for Prague only.
Ln 158: scientific notation. Here you use “ms −1” which is perfectly correct, but before I also have seen “m/s” and “g/kg”. Please make consistent throughout the whole manuscript. In this piece of the analysis maybe underline that the diagnostic 10-m wind speed is analsysed.
Ln 162: most important component of UMI in view of human living impact. Is that correct? It is the most used and most well-known UMI component, but the wind island is also important for ventilation against heat and air pollution… Maybe reword.
Figure 8: Caption: Maybe advance the caption towards “Modelled diurnal cycle of 2-m UHI intensity in dependence on urban fraction for city of Prague (or all cities if all cities were used)” . Similar upgrades to the caption can be applied to Figures 9 and 10. Also indicate whether the time axis is in UTC or local time. The model domain is wide enough to have an hour difference in start of the UHI between the west and the east of the domain so this may affect your statistics.
Figure 8: Could error bars or confidence intervals be added to these plots.4
Ln 169: it is important to remark that only the BEP+BEM scheme generates a negative UHI in the early morning till noon, while SLUCM does not. This is important information, since this urban cool island has been observed in multiple cities across Europe. Apparently BEP+BEM does a much better thing in representing this dynamics.
Ln 187: SLUCM assumes constant anthropogenic heat independent to outdoor conditions. Although this is true, SLUCM does have a diurnal and seasonal cycle on AH implemented, which may still favour the simulation.
Reference:
Steeneveld, G.J., J.O. Klompmaker, R.J. Groen,. A.A.M. Holtslag, 2018: An Urban Climate Assessment and Management tool for combined heat and air quality judgements at neighbourhood scales. Resources, Conservation, and Recycling, 132, 204-217.
Citation: https://doi.org/10.5194/egusphere-2025-388-RC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 84 | 24 | 7 | 115 | 6 | 5 |
- HTML: 84
- PDF: 24
- XML: 7
- Total: 115
- BibTeX: 6
- EndNote: 5
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 34 | 31 |
| China | 2 | 12 | 11 |
| Germany | 3 | 9 | 8 |
| France | 4 | 7 | 6 |
| Netherlands | 5 | 6 | 5 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 34